Determination of transcription accuracy

ABSTRACT

A method may include obtaining audio of a communication session between a first device of a first user and a second device of a second user. The method may further include obtaining a transcription of second speech of the second user. The method may also include identifying one or more first sound characteristics of first speech of the first user. The method may also include identifying one or more first words indicating a lack of understanding in the first speech. The method may further include determining an experienced emotion of the first user based on the one or more first sound characteristics. The method may also include determining an accuracy of the transcription of the second speech based on the experienced emotion and the one or more first words.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/394,316, filed on Apr. 25, 2019, the disclosure of which isincorporated herein by reference in its entirety.

FIELD

The embodiments discussed herein are related to determination oftranscription accuracy.

BACKGROUND

Modern telecommunication services provide features to assist those whoare deaf or hearing-impaired. One such feature is a text captionedtelephone system for the hearing-impaired. A text captioned telephonesystem may include a telecommunication intermediary service that isintended to permit a hearing-impaired user to utilize a normal telephonenetwork.

The subject matter claimed herein is not limited to embodiments thatsolve any disadvantages or that operate only in environments such asthose described above. Rather, this background is only provided toillustrate one example technology area where some embodiments describedherein may be practiced.

SUMMARY

A method may include obtaining audio of a communication session betweena first device of a first user and a second device of a second user. Themethod may further include obtaining a transcription of second speech ofthe second user. The method may also include identifying one or morefirst sound characteristics of first speech of the first user. Themethod may also include identifying one or more first words indicating alack of understanding in the first speech. The method may furtherinclude determining an experienced emotion of the first user based onthe one or more first sound characteristics. The method may also includedetermining an accuracy of the transcription of the second speech basedon the experienced emotion and the one or more first words.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described and explained with additionalspecificity and detail through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example environment to verify transcriptions of acommunication session;

FIG. 2 illustrates an example verified transcription;

FIG. 3 illustrates an example communication device that may be used toverify transcriptions of a communication session; and

FIG. 4 is a flowchart of an example computer-implemented method todetect accuracy of transcriptions of a communication session.

DESCRIPTION OF EMBODIMENTS

Some embodiments in this disclosure relate to a method and/or systemthat may verify the transcriptions of a communication session. In theseand other embodiments, the transcriptions may include the text of thewords spoken by one or more parties in a communication session.Transcriptions of a communication session may include inaccuracies. Forexample, a participant in a communication session may speak quickly suchthat a transcription system misses one or more words spoken orincorrectly transcribes one or more words spoken. Alternatively, atranscription system may be configured to understand particular accentsand may be less accurate with other accents. For example, a participantin a communication session may speak quickly and may also have an accentfor which a transcription system is not configured. The transcriptionsystem may generate a transcription of the participant's speech in thecommunication session but may not accurately capture each of theparticipant's words. In some embodiments, errors in transcriptions mayalso occur because of background noise in the communication session,such as multiple participants speaking at the same time or outside noisesuch as, for example, vehicular traffic, animals, children, or wind.

Some embodiments in this disclosure describe a system that may beconfigured to verify transcriptions of communication sessions. In someembodiments, verifying transcriptions may include determining theaccuracy of transcriptions of communication sessions. For example, auser may participate in a communication session by reading from atranscription of a third-party user's speech and speaking to thethird-party user. The system may obtain audio of a communicationsession, which may include speech of the user and the third-party user.The system may also obtain a transcription of the audio. In someembodiments, the system may determine the accuracy of the transcriptionbased on the emotions experienced by the user and words indicating alack of understanding by the user. For example, if the user isexperiencing a frustrated or angry emotion, the system may determine thetranscription of the communication session is less accurate than if theuser is experiencing happiness or no frustration. Alternatively, if theuser uses many words indicating a lack of understanding, the system maydetermine that the transcription of the communication session is notaccurate.

In some embodiments, the system may be configured to obtain data fromthe third-party user and may determine the accuracy of the transcriptionbased on the data from the third-party user together with the data fromthe user. In some embodiments, the accuracy of the transcription may beprovided to a transcription system that generated the transcription. Theaccuracy of the transcription may assist the transcription system toimprove the accuracy of the transcription. In some embodiments, thetranscription system may be an automated transcription system.Alternately or additionally, the transcription system may be a re-voicedtranscription system that generates the transcription based on are-voicing of the audio by a human operator. In these and otherembodiments, the accuracy of the transcription may be presented to thehuman operator to improve the ability of the human operator to re-voicethe communication session.

In some embodiments, the systems and/or methods described in thisdisclosure may thus help to determine the accuracy of a transcription ofa communication session and may help to improve transcribingcommunication sessions. Thus, the systems and/or methods described inthis disclosure may provide at least a technical solution to a technicalproblem associated with the transcription of communication sessions inthe technology of telecommunications.

FIG. 1 illustrates an example environment 100 to verify transcriptionsof a communication session. The environment 100 may be arranged inaccordance with at least one embodiment described in the presentdisclosure. The environment 100 may include a network 105, a user device110 associated with a user 115, a determination system 130, athird-party device 140 associated with a third-party user 145, a camera150, and a transcription system 160.

The network 105 may be configured to communicatively couple one or moreof the user device 110, the determination system 130, the third-partydevice 140, the camera 150, and the transcription system 160 with one ormore of the user device 110, the determination system 130, thethird-party device 140, the camera 150, and the transcription system160. For example, the user device 110 may be communicatively coupledwith the third-party device 140 by way of the network 105 and may becommunicatively coupled with the transcription system 160. However, thetranscription system 160 may not be communicatively coupled with thethird-party device 140.

In some embodiments, the network 105 may be any network or configurationof networks configured to send and receive communications betweensystems and devices. In some embodiments, the network 105 may include awired network or wireless network, and may have numerous differentconfigurations. In some embodiments, the network 105 may also be coupledto or may include portions of a telecommunications network, includingwireless telecommunications, voice over internet protocol networks, andtelephone lines such as a public switch telephone network (PSTN) line,for sending data in a variety of different communication protocols, suchas a protocol used by a plain old telephone system (POTS), cellular anddata networks, such as long-term evolution (LTE) networks and 5Gnetworks, among other protocols.

Each of the user device 110 and the third-party device 140 may be anyelectronic or digital computing device. For example, each of the userdevice 110 and the third-party device 140 may include a desktopcomputer, a laptop computer, a smartphone, a mobile phone, a tabletcomputer, a telephone, a phone console, or any other computing device.In some embodiments, the user device 110 may be a captioning telephonethat is configured to present transcriptions of a communication session,such as one of the CaptionCall® 57T model family or 67T model family ofcaptioning telephones or a device running the CaptionCall® mobile app.For example, in some embodiments, the user device 110 may include avisual display that is integral with the user device 110 and that isconfigured to present text transcriptions of a communication session.

In some embodiments, the user device 110 and the third-party device 140may be configured to establish communication sessions with otherdevices. For example, each of the user device 110 and the third-partydevice 140 may be configured to establish an outgoing audio or videocall with another device. For example, the audio call may be a telephonecall over a wireless cellular network, a wired Ethernet network, anoptical network, or a POTS line. For example, the user device 110 maycommunicate over a wireless cellular network and the third-party device140 may communicate over a PSTN line. Alternatively or additionally, theuser device 110 and the third-party device 140 may communicate overother wired or wireless networks that do not include or only partiallyinclude a PSTN. For example, a telephone call or communication sessionbetween the user device 110 and the third-party device 140 may be aVoice over Internet Protocol (VoIP) telephone call.

In some embodiments, each of the user device 110 and the third-partydevice 140 may be configured to obtain audio during a communicationsession. The audio may be part of a video communication or an audiocommunication. As used in this disclosure, the term audio may be usedgenerically to refer to sounds that may include spoken words.Furthermore, the term audio may be used generically to include audio inany format, such as a digital format, an analog format, or a propagatingwave format. Furthermore, in the digital format, the audio may becompressed using different types of compression schemes. Also, as usedin this disclosure, the term video may be used generically to refer to acompilation of images that may be reproduced in a sequence to producevideo.

As an example of obtaining audio, the user device 110 may be configuredto obtain first audio from a user 115. In some embodiments, the user 115may be a hearing-impaired user. As used in the present disclosure, a“hearing-impaired user” may refer to a person with diminished hearingcapabilities. Hearing-impaired users often have some level of hearingability that has usually diminished over a period of time such that thehearing-impaired user can communicate by speaking, but that thehearing-impaired user often struggles in hearing and/or understandingothers. Alternatively or additionally, in some embodiments, the user 115may not be a hearing-impaired user.

The first audio may include speech of the user 115. The speech of theuser 115 may be words spoken by the user 115. For example, the userdevice 110 may obtain speech of the user 115 from a microphone of theuser device 110 or from another device that is communicatively coupledto the user device 110.

The third-party device 140 may also be configured to obtain second audiofrom a third-party user 145. The second audio may include speech of thethird-party user 145. The speech of the third-party user 145 may bewords spoken by the third-party user 145. In some embodiments, thethird-party device 140 may obtain the second audio from a microphone ofthe third-party device 140 or from another device communicativelycoupled to the third-party device 140. During the communication session,the user device 110 may provide the first audio obtained by thethird-party device 140. Alternatively or additionally, the third-partydevice 140 may provide the second audio obtained by the user device 110.Thus, during a communication session, both the user device 110 and thethird-party device 140 may obtain both the first audio from the user 115and the second audio from the third-party user 145.

One or both of the user device 110 and the third-party device 140 may beconfigured to provide the first audio, the second audio, or both thefirst audio and the second audio to the determination system 130 and/orthe transcription system 160.

In some embodiments, the transcription system 160 may be configured toobtain a transcription of audio received from either one or both of theuser device 110 and the third-party device 140. The transcription system160 may also provide the transcription of the audio to either one orboth of the user device 110 and the third-party device 140. Alternatelyor additionally, the transcription system 160 may provide thetranscription to the determination system 130.

Either one or both of the user device 110 and the third-party device 140may be configured to present the transcription received from thetranscription system 160. For example, the user device 110 may beconfigured to display the received transcriptions on a display that ispart of the user device 110 or that is communicatively coupled to theuser device 110.

In some embodiments, the transcription system 160 may include a speechrecognition system. The speech recognition system may be configured togenerate transcriptions from audio. In these and other embodiments, thespeech recognition system may include any configuration of hardware,such as processors, servers, and database servers that are networkedtogether and configured to perform a task. For example, the speechrecognition system may include one or multiple computing systems, suchas multiple servers that each include memory and at least one processor.Alternately or additionally, the speech recognition system may beseparate from the transcription system 160 and may provide thetranscriptions to the transcription system 160.

In some embodiments, the speech recognition system may be an automaticsystem that automatically recognizes speech independent of humaninteraction to generate the transcription. In these and otherembodiments, the speech recognition system may include speech enginesthat are trained to recognize speech. The speech engine may be trainedfor general speech and not specifically trained using speech patterns ofthe participants in the communication session. Alternatively oradditionally, the speech engine may be specifically trained using speechpatterns of one or both of the participants of the communicationsession.

Alternatively or additionally, the speech recognition system may be are-voicing system. In these and other embodiments, the speechrecognition system may broadcast the audio and obtain a re-voicing ofthe audio as re-voiced audio. In these and other embodiments, a personmay listen to the broadcast audio and re-speak the words of the audio.The re-spoken words may be the re-voiced audio. The speech recognitionsystem may provide the re-voiced audio to a speech engine that istrained for the person. The speech engine may generate thetranscription.

In these and other embodiments, the speech recognition system may beconfigured to recognize speech in the audio. Based on the recognizedspeech, the speech recognition system may generate the transcription ofthe speech. The transcription may be a written version of the speech inthe audio. The transcription system 160 may provide the transcription ofthe speech to one or more of the user device 110, the third-party user145, and/or the determination system 130.

In some embodiments, the speech recognition system may be configured toreceive the audio in real-time or substantially real-time. In these andother embodiments, the speech recognition system may be configured togenerate the transcription in real-time or substantially real-time.

The camera 150 may be a still or video camera positioned to recordimages of the user 115. For example, in some embodiments, the camera 150may record images of the face of the user 115. In these and otherembodiments, the camera 150 may record images of the user 115 during acommunication session between the user 115 and a third-party user 145.In some embodiments, the camera 150 may record images of the user 115outside of the visible spectrum of light, such as infrared images orx-ray images. In some embodiments, the environment 100 may includemultiple cameras 150. For example, in some embodiments, the camera 150may be positioned to record images of the user 115 and another cameramay be positioned to record images of the third-party user 145. In someembodiments, the camera 150 may be a part of the user device 110 and/orthe third-party device 140. For example, in some embodiments, the userdevice 110 may be a smart cellular telephone device and the camera 150may be a front-facing camera near a screen of the device. In someembodiments, the camera 150 and/or the other camera may record multipleimages. The multiple images may be recorded sequentially to form avideo.

The determination system 130 may be configured to determine the accuracyof a transcription of audio from a communication session between theuser device 110 and the third-party device 140. The transcription of theaudio may be generated by the transcription system 160. In someembodiments, the determination system 130 may include any configurationof hardware, such as processors, servers, and database servers that arenetworked together and configured to perform a task. For example, thedetermination system 130 may include one or multiple computing systems,such as multiple servers that each include memory and at least oneprocessor.

In some embodiments, audio of a communication session between the userdevice 110 and the third-party device 140 may be provided to thedetermination system 130. Alternately or additionally, the determinationsystem 130 may obtain images of the user 115 and/or the third-party user145. Alternately or additionally, the determination system 130 mayobtain the transcription of the audio of the communication session. Insome embodiments, the determination system 130 may use the audio, theimages, and/or the transcription of the audio to determine the accuracyof the transcription.

For example, in some embodiments, the determination system 130 may beconfigured to determine the accuracy of the transcription based on theaudio of the communication session. In these and other embodiments, thedetermination system 130 may determine an emotion of the user 115 and/orthe third-party user 145 using the audio of the communication session.Based on the emotion of the user 115, the determination system 130 maydetermine the accuracy of the transcription. For example, when thedetermined emotion of the user 115 and/or the third-party user 145indicates irritation, anger, or other analogous type emotions, thedetermination system 130 may determine that the accuracy of thetranscription is below a threshold or lower than an expected accuracy ofthe transcription because the lower accuracy of the transcription maycause the determined emotion of the user 115 and/or the third-party user145.

In these and other embodiments, the audio may include speech of the user115 and speech of the third-party user 145. The speech of the user 115and the speech of the third-party user 145 may include multiple soundcharacteristics. For example, the sound characteristics of the speech ofthe user 115 may include a tone, a volume, a pitch, an inflection, atimbre, and a speed. The tone may include a particular pitch or changeof pitch of the speech, where the pitch represents how “low” or “high”the sound is based on the frequency of the sound vibrations. A greaterfrequency such as 16 kiloHertz (kHz) may correspond with a higher pitchthan a lower frequency such as 100 Hertz (Hz). The volume of the speechis the loudness of the speech. Inflection may include changes in thepitch or volume of speech. For example, inflection may include anincreasing volume of speech or a decreasing pitch of speech. Timbre mayinclude the perceived quality of different sounds. For example, thetimbre of a trumpet may be different from the timbre of a flute. Thespeech of one individual may have a different timbre than the speech ofanother individual. The speed of the speech may include how many wordsper minute an individual is saying. For example, in ordinary speech inEnglish, an individual may average speaking approximately one hundredand thirty words per minute.

An individual may alter his or her speech in response to differentemotions or in an attempt to increase the understanding of other partiesto a conversation. For example, an individual may alter the speed atwhich he or she speaks if the individual is agitated, frustrated,confused, or perceives that he or she is not understanding anotherperson or is not being understood by the other person. An individual whoperceives that he or she is not being understood by another person mayincrease the volume and reduce the speed of his or her speech.Additionally, if an individual is angry, the individual may increase thevolume and increase the speed of his or her speech.

In some embodiments, the determination system 130 may identify multiplesound characteristics in the speech of the user 115. For example, thedetermination system 130 may identify the tones, volumes, pitches,inflections, timbres, and speeds of the speech of the user 115 duringthe course of a communication session. The determination system 130 mayinclude a sound-level meter and a frequency analyzer. The sound-levelmeter may determine a volume of the speech. The frequency analyzer maydetermine the wave pattern of the speech. Using the wave pattern of thespeech, the determination system 130 may determine the tone, pitch,inflection, timbre, and other aspects of the speech.

In some embodiments, the determination system 130 may be configured todetermine emotions of the user 115 based on the sound characteristics ofthe speech of the user 115. For example, if the determination system 130determines that the user 115 is speaking loudly with a pitch thatreflects agitation, the determination system 130 may determine that theuser 115 is upset and that the transcriptions are inaccurate.

In some embodiments, the sound characteristics of the speech may varyover the course of the communication in response to external factors orin response to the content of the communication session. For example,the user 115 may speak more loudly if the user 115 is in a noisyenvironment than the user 115 would speak in a quiet environment.Additionally, the user 115 may speak more loudly in response to thethird-party user 145 requesting the user 115 to speak up. In these andother embodiments, the determination system 130 may consider theexternal factors or content of the communication session whendetermining the accuracy of the transcription based on the speech of theuser 115.

In these and other embodiments, the determination system 130 maydetermine that the user 115 is experiencing a particular emotion byusing a machine learning algorithm. For example, in some embodiments,the determination system 130 may use hidden Markov models (HMM), dynamictime warping (DTW)-based speech recognition, neural networks, deepfeedforward and recurrent neural networks, or other forms of machinelearning to develop a machine learning model that determines emotionsthat the user 115 may be experiencing based on the sound characteristicsof the speech of the user 115. For example, the determination system 130may determine that a rising volume of the speech of the user 115indicates that the user 115 is frustrated. Based on the indication thatthe user 115 is frustrated, the determination system 130 may determinethe accuracy of the transcription.

In some embodiments, the determination system 130 may develop a machinelearning model that may be used to determine the accuracy of thetranscription that uses as an input the emotion of the user 115 andexternal factors or content of the communication session. In these andother embodiments, the determined emotion of the user 115, the contentof the communication session based on the transcription, and theexternal factors, such as the background noise in the audio and the timeof day, may be provided to the machine learning model. The machinelearning model may make a determination of whether the transcription isaccurate or inaccurate with a confidence score. When the machinelearning model indicates that the transcription includes inaccuracieswith a confidence score above a threshold, the determination system 130may determine that the transcription is inaccurate. In these and otherembodiments, a determination that the transcription is inaccurate mayindicate that the transcription includes one or more inaccurate wordsand one or more accurate words. For example, a determination that thetranscription is inaccurate may indicate that enough of the words of thetranscription or enough of those words that provide context of thetranscription are inaccurate to reduce the ability to understand thetranscription and/or understand the context of the conversationreflected in the transcription even when many of the words of thetranscription are an accurate transcription.

In some embodiments, the determination system 130 may also obtain imagedata of the communication session between the user device 110 and thethird-party device 140 from the camera 150. As described above, theimage data may include still images or video of the user 115 both in therange of light visible to human eyes and outside the range of visiblelight. The image data may include images of the face of the user 115. Insome embodiments, the determination system 130 may be configured toidentify facial expressions of the user 115 based on the image data. Forexample, in some embodiments, the determination system 130 may useartificial intelligence algorithms such as, for example, classificationalgorithms including neural networks, decision trees, and quadraticdiscriminant analysis; clustering algorithms including K-meansclustering and deep learning models; ensemble learning algorithmsincluding ensemble averaging; Bayesian networks; Markov random fields;regression algorithms including Gaussian process regression; sequencelabelling algorithms; and other machine learning algorithms, to identifyfacial expressions of the user 115 based on the image data. In these andother embodiments, the determination system 130 may identify facialexpressions of the user 115 including smiles, frowns, puzzledexpressions, furrowed brows, combinations of features of eyebrows andlips suggesting anger or frustration, and other facial expressions. Inthese and other embodiments, facial expressions of the user 115 mayinclude the positioning of the lips, eyebrows, eyes, chin, nostrils,nose, ears, and/or other modifications of the facial features of theuser 115.

In some embodiments, the determination system 130 may determine that theuser 115 is experiencing multiple different emotions based on the soundcharacteristics and the facial expressions. For example, thedetermination system 130 may determine the user 115 is experiencinganger during the communication session based on the tone of voice of theuser 115 and based on the user 115 drawing in his eyebrows, lowering hiseyebrows, pressing his lips firmly, and/or bulging his eyes.Alternatively or additionally, in some embodiments, the determinationsystem 130 may determine the user 115 is experiencing surprise based onthe user 115 opening her eyes wide, arching her eyebrows, and/ordropping her jaw. In some embodiments, the determination system 130 maydetermine the user 115 is experiencing frustration based on the user 115pressing his lips together and/or raising his chin. The determinationsystem 130 may also determine the user 115 is experiencing otheremotions based on the tones, volumes, pitches, inflections, timbres, andspeeds of the speech of the user 115 and based on the facial features,such as the positioning of the lips, eyebrows, eyes, chin, nostrils,nose, and/or ears of the user 115.

In some embodiments, the determination system 130 may develop a machinelearning model that may be used to determine the accuracy of thetranscription that uses as an input the emotion of the user 115, theimages of the user 115, and/or external factors or content of thecommunication session as described above. In these and otherembodiments, the determination system 130 may use the machine learningmodel to determine the accuracy of the transcription.

In some embodiments, the determination system 130 may be configured todetermine the accuracy of the transcription based on the transcription.For example, the determination system 130 may determine the accuracy ofa first portion of the transcription based on a second portion of thetranscription that is correctly transcribed.

For example, the determination system 130 may determine that the firstportion of the transcription includes inaccuracies based on the secondportion of the transcription including accurately transcribed words thatmay reflect on the accuracy of the first portion of the transcription.In these and other embodiments, the determination system 130 may notdetermine the accuracy of the first portion of the transcription throughanalysis of the first portion of the transcription. Rather, thedetermination system 130 may infer the accuracy of the first portion ofthe transcription based on the meaning of words accurately transcribedin the second portion of the transcription.

In some embodiments, the determination system 130 may determine that thetranscription is inaccurate based on the frequency of occurrence of theaccurately transcribed words that may reflect on the accuracy of thefirst portion of the transcription. For example, when a frequency ofoccurrence of the words is above a threshold, the determination system130 may determine that the transcription is inaccurate.

For example, the determination system 130 may identify words indicatinga lack of understanding in the speech of the user 115 in thetranscription. For example, the user 115 may use question words, makerequests for the third-party user 145 to repeat portions of thethird-party user 145's speech or to alter sound characteristics of thespeech, or may apologize. For example, the determination system 130 mayidentify that the user 115 responds “what” to phrases made by thethird-party user 145. Alternatively, the user 115 may respond with otherquestion words such as “when,” “where,” “how,” “why,” “who,” or “whom.”Such words and similar words may indicate a lack of understanding on thepart of the user 115. Additionally, in some embodiments, thedetermination system 130 may identify requests made by the user 115 forthe third-party user 145 to repeat portions of the speech of thethird-party user 145 or to alter the sound characteristics of thespeech. These requests may include phrases such as “could you repeatthat,” “I didn't understand,” “come again,” can you please say thatagain,” “could you speak louder,” “speak up, please,” “could you talkslower,” or similar phrases. Alternatively or additionally, the user 115may apologize. Apologies may include phrases such as “I'm sorry,”“forgive me,” and other phrases. The determination system 130 mayidentify the words using a matching algorithm that looks for the wordsin the transcription that match words in a database that indicate a lackof understanding, such as the words identified above. Alternately oradditionally, the determination system 130 may identify words indicatinga lack of understanding in the speech of the user 115 using artificialintelligence such as, for example, HMM, DTW-based speech recognition,neural networks, deep feedforward and recurrent neural networks, orother algorithms.

Another example of words that may indicate a lack of understanding inthe speech of the user 115 in the transcription may include thethird-party user 145 repeating words or phrases during the communicationsession. For example, the third-party user 145 may repeat the samephrase multiple times or may repeat similar phrases multiple times,which may indicate the third-party user 145 thinks the user 115 does notunderstand what the third-party user 145 is saying. In these and otherembodiments, the determination system 130 may identify repeated words orphrases by maintaining history of the words or phrases for a period oftime, such as words or phrases that occur over the last ten, fifteen,twenty, thirty, or more seconds of a communication session. Thedetermination system 130 may compare the words in the history todetermine if the words are repeated.

The determination system 130 may also obtain sound characteristics ofthe speech of the third-party user 145. The third-party user 145 mayalter their speech based on a perception of whether the user 115understands the third-party user 145. For example, the third-party user145 may speak more slowly in an effort to increase the perceivedunderstanding of the user 115. Alternatively, the third-party user 145may speak at a higher volume. Alternatively or additionally, thethird-party user 145 may speak more slowly and at a higher volume. Thedetermination system 130 may identify the sound characteristics of thespeech of the third-party user 145 in an analogous manner as theidentifying sound characteristics of the speech of the user 115. Thedetermination system 130 may determine an accuracy of the transcriptionusing the sound characteristics of the speech of the third-party user145 in an analogous manner as determining an accuracy of thetranscription using the sound characteristics of the speech of the user115.

In some embodiments, the determination system 130 may correlate thewords indicating a lack of understanding and the sound characteristicsof the speech with particular words and/or phrases in the transcriptionof the communication session and determine that specific portions of thetranscription are not accurate. For example, if the soundcharacteristics of the speech associated with frustration and confusionand the words indicating a lack of understanding begin at a firstparticular time and end at a second particular time during thecommunication session, the determination system 130 may determine thetranscription is not accurate between the first particular time and thesecond particular time.

In some embodiments, the determination system 130 may determine a topicof the communication session. In these and other embodiments, thedetermination system 130 may determine the topic of the communicationsession by parsing the transcription into terms and identifying terms ofprominence based on a prominence score for each of the terms. The termswith higher prominence may be provided to a machine learning model thatmay output the topic with a particular confidence score. In someembodiments, a topic with a particular confidence score above athreshold may be used by determination system 130.

In some embodiments, the determination system 130 may also determine anexpected emotion for the user 115 based on the topic of thecommunication session. For example, the determination system 130 mayconsult a look-up table that associates topics with expected emotions.Some topics of conversation may generally elicit particular emotionalresponses from individuals. For example, if the third-party user 145 issharing news regarding cancer, death, or bad health with the user 115,the user 115 may be sad or frustrated. Alternatively or additionally, ifthe third-party user 145 is sharing news regarding a reward, job offer,or new purchase with the user 115, the user 115 may be happy. Theexpected emotion may change during the course of the communicationsession. For example, the third-party user 145 and the user 115 maybegin a communication session with greetings which may generally beassociated with positive emotions. The third-party user 145 may thenshare unfortunate news with the user 115 and the expected emotion forthe user 115 may be surprise or sadness. Later, the third-party user 145may share positive news with the user 115 and the expected emotion forthe user 115 may be joy. In these and other embodiments, thedetermination system 130 may determine an expected emotion for the user115 at multiple times during the communication session.

The determination system 130 may further determine an unexpected emotionfor the user 115. The unexpected emotion may be a measure of thedifference between the emotion the user 115 experiences and the emotionthe user 115 would be expected to experience based on the topic of thecommunication session. For example, the third-party user 145 may sharepositive news with the user 115 such that the user 115 would be expectedto experience joy but the user 115 may actually experience frustration.The determination system 130 may determine that the frustration the user115 is experiencing is an unexpected emotion not associated with thetopic of the communication session. In these and other embodiments, thedetermination system 130 may attribute the unexpected emotion toaccuracy of the transcription of the communication session.

In some embodiments, the unexpected emotion may be calculated based onassigning each of the emotions a score. The scores of similar emotionsmay be the same or similar. For example, joy and happy may assigned ascore of six, content and mellow may be assigned a score of three, andfrustrated, sad, and mad may be assigned a score of zero. When adifference between the emotion the user 115 experiences and the emotionthe user 115 would be expected to experience is greater than three, thedetermination system 130 may determine that the transcription isinaccurate.

In some embodiments, the determination system 130 may further determinewhether the user 115 is viewing the transcription. For example, in someembodiments, the camera 150 may be positioned to record images of theuser 115. In these and other embodiments, the determination system 130may use images from the camera 150 for eye-tracking to determine whetherthe eyes of the user 115 are directed to a location on a screen of theuser device 110 where the transcription may be presented. In response todetermining that the eyes of the user 115 are not directed at thetranscription, the determination system 130 may determine the user 115is not viewing the transcription. In response to determining the user115 is not viewing the transcription, the determination system 130 maydetermine the factors discussed in this disclosure, such as the words ofthe user 115 indicating a lack of understanding, the soundcharacteristics of the speech of the third-party user 145, and/or theexperienced emotion of the user 115 are not related to the accuracy ofthe transcription. In these and other embodiments, the determinationsystem 130 may determine the user 115 is directing the eyes of the user115 at the transcription when the user 115 directs the eyes toward thetranscription at a particular frequency or over a particular period oftime. For example, the user 115 may periodically look away from thetranscription, such as in response to an outside noise or to look at animage displayed on the user device 110, and may still be determined tobe viewing the transcription.

In some embodiments, the determination system 130 may determine that theuser 115 is viewing the transcription during a first duration of timebased on determining that the eyes of the user 115 are directed at thetranscription during the first duration of time and may determine thatthe user 115 is not viewing the transcription during a second durationof time based on determining that the eyes of the user 115 are notdirected at the transcription during the second duration of time. Inthese and other embodiments, the determination system 130 may determinethe factors discussed in this disclosure are related to the accuracy ofthe transcription during the first duration of time but not during thesecond duration of time.

In some embodiments, the determination system 130 may determine accuracyof transcription based on a combination of the factors discussed in thisdisclosure. For example, factors may include sound characteristics ofthe speech of the user 115, the expected emotion of the user 115, thewords of the user 115 indicating a lack of understanding, the soundcharacteristics of the speech of the third-party user 145, and/or therepeated words of the third-party user 145. In some embodiments, thedetermination system 130 may train a machine learning model using thesefactors. The machine learning model may output an indication of whetherthe transcription is accurate. For example, the machine learning modelusing one or more of the factors discussed above may classify atranscription of the audio, which results in the factors provided to themodel, as accurate or inaccurate. Furthermore, the machine learningmodel may classify portions of a transcription of the audio as accurateor inaccurate. For example, a first portion of the transcription mayresult from a first portion of audio from a communication session. Thefirst portion of the transcription and the first portion of the audiomay results in first factors that may result in the classification ofthe first portion of the transcription as accurate. A second portion ofthe transcription may result from a second portion of audio from thecommunication session. The second portion of the transcription and thesecond portion of the audio may results in second factors that mayresult in the classification of the second portion of the transcriptionas inaccurate.

Modifications, additions, or omissions may be made to the environment100 without departing from the scope of the present disclosure. Forexample, in some embodiments, the determination system 130 and thetranscription system 160 may be one system. Alternatively oradditionally, in some embodiments, the environment 100 may not includethe camera 150. Alternately or additionally, the determination system130 may be included in the user device 110 or the third-party user 145.Alternately or additionally, the determination system 130 may bedistributed across multiple devices such as the user device 110 and thetranscription system 160.

FIG. 2 illustrates an example verified transcription 200 of acommunication session. The verified transcription 200 may includemultiple lines of text 205. In some embodiments, the lines of text 205may be generated by a transcription system such as the transcriptionsystem 160 of FIG. 1 . The lines of text 205 may correspond with thewords of the speech of the user 115 and/or the third-party user 145 ofFIG. 1 . In some embodiments, a determination system, such as thedetermination system 130 of FIG. 1 , may verify the accuracy of thelines of text 205. In these and other embodiments, the determinationsystem may identify multiple portions 210 a, 210 b, and 210 c of thetext that are not accurate. In these and other embodiments, the verifiedtranscription 200 may include boxes 215 a, 215 b, and 215 c surroundingthe portions 210 a, 210 b, and 210 c. Alternatively or additionally, insome embodiments, the boxes 215 a, 215 b, and 215 c may includehighlighting, underlining, italicizing, bolding, increasing a font size,or other alterations to the formatting of the portions 210 a, 210 b, and210 c.

In some embodiments, the lines of text 205 of a transcription may begenerated by a transcription system. In these and other embodiments, adetermination system, such as the determination system 130 of FIG. 1 ,may provide the verified transcription 200 to the transcription system.In some embodiments, the determination system may provide the verifiedtranscription 200 to the transcription system in real-time, i.e., duringthe course of a communication session. In these and other embodiments,the verified transcription 200 may facilitate the transcription systemin identifying areas for improvement in the transcribing of thecontemporaneous communication session and for improvement of futuretranscriptions.

Modifications, additions, or omissions may be made to the verifiedtranscription 200 without departing from the scope of the presentdisclosure. For example, in some embodiments, the lines of text 205 maybe presented in a single column. Alternatively or additionally, in someembodiments, the lines of text 205 may also include an identity of aspeaker of the lines of text 205.

FIG. 3 illustrates an example communication device 300 that may be usedin verifying transcriptions of communication sessions. The communicationdevice 300 may be arranged in accordance with at least one embodimentdescribed in the present disclosure. The communication device 300 mayinclude a processor 305, a memory 310, a communication interface 315, adisplay 320, a user interface unit 325, and a peripheral device 330,which all may be communicatively coupled. In some embodiments, thecommunication device 300 may be part of any of the systems or devicesdescribed in this disclosure. For example, the communication device 300may be part of any of the user device 110, the determination system 130,the third-party device 140, the camera 150, and the transcription system160 of FIG. 1 . In some embodiments, the communication device 300 may bepart of a phone console.

Generally, the processor 305 may include any suitable special-purpose orgeneral-purpose computer, computing entity, or processing deviceincluding various computer hardware or software modules and may beconfigured to execute instructions stored on any applicablecomputer-readable storage media. For example, the processor 305 mayinclude a microprocessor, a microcontroller, a digital signal processor(DSP), an application-specific integrated circuit (ASIC), aField-Programmable Gate Array (FPGA), or any other digital or analogcircuitry configured to interpret and/or to execute program instructionsand/or to process data, or any combination thereof.

Although illustrated as a single processor in FIG. 3 , it is understoodthat the processor 305 may include any number of processors distributedacross any number of networks or physical locations that are configuredto perform individually or collectively any number of operationsdescribed herein. In some embodiments, program instructions may beloaded into the memory 310. In these and other embodiments, theprocessor 305 may interpret and/or execute program instructions and/orprocess data stored in the memory 310. For example, the communicationdevice 300 may be part of the user device 110, the determination system130, the third-party device 140, the camera 150, and the transcriptionsystem 160 of FIG. 1 . In these and other embodiments, the programinstructions may include the processor 305 processing a transcription ofa communication session and verifying its accuracy.

The memory 310 may include computer-readable storage media for carryingor having computer-executable instructions or data structures storedthereon. Such computer-readable storage media may be any available mediathat may be accessed by a general-purpose or special-purpose computer,such as the processor 305. By way of example, and not limitation, suchcomputer-readable storage media may include non-transitorycomputer-readable storage media including Read-Only Memory (ROM),Electrically Erasable Programmable Read-Only Memory (EEPROM), CompactDisc Read-Only Memory (CD-ROM) or other optical disk storage, magneticdisk storage or other magnetic storage devices, flash memory devices(e.g., solid state memory devices), or any other storage media which maybe used to carry or store desired program code in the form ofcomputer-executable instructions or data structures and which may beaccessed by a general-purpose or special-purpose computer. Combinationsof the above may also be included within the scope of computer-readablestorage media. Computer-executable instructions may include, forexample, instructions and data configured to cause the processor 305 toperform a certain operation or group of operations, such as one or moreblocks of the method 400. Additionally or alternatively, in someembodiments, the instructions may be configured to cause the processor305 to perform the operations of the environment 100 of FIG. 1 to verifytranscriptions of a communication session. In these and otherembodiments, the processor 305 may be configured to execute instructionsto verify the transcription of a communication session.

The communication interface 315 may include any component, device,system, or combination thereof that is configured to transmit or receiveinformation over a network. In some embodiments, the communicationinterface 315 may communicate with other devices at other locations, thesame location, or even other components within the same system. Forexample, the communication interface 315 may include a modem, a networkcard (wireless or wired), an infrared communication device, a wirelesscommunication device (such as an antenna), and/or chipset (such as aBluetooth device, an 802.6 device (e.g., Metropolitan Area Network(MAN)), a WiFi device, a WiMax device, cellular communicationfacilities, etc.), plain old telephone service (POTS), and/or the like.The communication interface 315 may permit data to be exchanged with anetwork and/or any other devices or systems described in the presentdisclosure.

The display 320 may be configured as one or more displays, like an LCD,LED, or other type display. The display 320 may be configured to presentvideo, text transcriptions, user interfaces, and other data as directedby the processor 305.

The user interface unit 325 may include any device to allow a user tointerface with the communication device 300. For example, the userinterface unit 325 may include a mouse, a track pad, a keyboard, atouchscreen, a telephone switch hook, a telephone keypad, volumecontrols, and/or other special purpose buttons, among other devices. Theuser interface unit 325 may receive input from a user and provide theinput to the processor 305.

The peripheral device 330 may include one or more devices. For example,the peripheral devices may include a microphone, a camera, and/or aspeaker, among other peripheral devices. In these and other embodiments,the microphone may be configured to capture audio. The imager may beconfigured to capture digital images. The digital images may be capturedin a manner to produce video or image data. In some embodiments, thespeaker may play audio received by the communication device 300 orotherwise generated by the communication device 300. In someembodiments, the processor 305 may be configured to process audiosignals and improve a signal-to-noise ratio of the audio signals, whichmay help reduce noise in the audio output by the speaker.

Modifications, additions, or omissions may be made to the communicationdevice 300 without departing from the scope of the present disclosure.

FIG. 4 is a flowchart of an example computer-implemented method toverify transcriptions of a communication session. The method 400 may bearranged in accordance with at least one embodiment described in thepresent disclosure. The method 400 may be performed, in whole or inpart, in some embodiments, by a system and/or environment, such as theenvironment 100 and/or the communication device 300 of FIGS. 1 and 3 ,respectively. In these and other embodiments, the method 400 may beperformed based on the execution of instructions stored on one or morenon-transitory computer-readable media. Although illustrated as discreteblocks, various blocks may be divided into additional blocks, combinedinto fewer blocks, or eliminated, depending on the desiredimplementation.

The method 400 may begin at block 405, where audio of a communicationsession between a first device of a first user and a second device of asecond user may be obtained. The communication session may be configuredfor verbal communication such that the audio includes first speech ofthe first user and second speech of the second user. In block 410, atranscription of the communication session may be obtained. In someembodiments, the transcription may be a transcription of the secondspeech of the second user.

In block 415, one or more first sound characteristics of the firstspeech may be identified. In some embodiments, the first soundcharacteristics may include a tone, a volume, a pitch, an inflection, atimbre, or a speed of the first speech. In block 420, one or more firstwords indicating a lack of understanding may be identified in the firstspeech. In some embodiments, the first words indicating a lack ofunderstanding may include requests for the second user to repeat one ormore portions of the second speech, question words, and apologies.

In block 425, an experienced emotion of the first user may be determinedbased on the first sound characteristics. In block 430, an accuracy ofthe transcription may be determined based on the experienced emotion andthe first words indicating a lack of understanding.

One skilled in the art will appreciate that, for this and otherprocesses, operations, and methods disclosed herein, the functionsand/or operations performed may be implemented in differing order.Furthermore, the outlined functions and operations are only provided asexamples, and some of the functions and operations may be optional,combined into fewer functions and operations, or expanded intoadditional functions and operations without detracting from the essenceof the disclosed embodiments. In some embodiments, the method 400 mayinclude additional blocks. For example, the method 400 may includeidentifying one or more second sound characteristics of the secondspeech may be identified. In some embodiments, the second soundcharacteristics may include an increasing volume of the second speech ora decreasing speed of the second speech. The method 400 may also includeidentifying one or more repeated words in the second speech. In theseand other embodiments, the accuracy of the transcription may bedetermined based on the experienced emotion, the first words indicatinga lack of understanding, the second sound characteristics, and therepeated words.

Alternatively or additionally, in some embodiments, the method 400 mayinclude obtaining image data of the communication session between thefirst device and the second device. The image data may include images ofthe first user. The method 400 may further include identifying one ormore facial expressions of the first user in the image data. In theseand other embodiments, the experienced emotion may be determined basedon the first sound characteristics and the facial expressions. In someembodiments, the method 400 may include determining a first topic of thecommunication session. The method may also include identifying anexpected emotion for the first user based on the first topic. The methodmay further include determining an unexpected emotion based on theexpected emotion and the experienced emotion. In these and otherembodiments, the accuracy of the transcription may be determined basedon the unexpected emotion and the first words. In some embodiments, themethod 400 may include determining the accuracy of the transcriptionbased on the unexpected emotion, the first words, the second soundcharacteristics, and the repeated words.

As indicated above, the embodiments described herein may include the useof a special purpose or general purpose computer (e.g., the processor305 of FIG. 3 ) including various computer hardware or software modules.Further, as indicated above, embodiments described herein may beimplemented using computer-readable media (e.g., the memory 310 of FIG.3 ) for carrying or having computer-executable instructions or datastructures stored thereon.

In some embodiments, the different components, modules, engines, andservices described herein may be implemented as objects or processesthat execute on a computing system (e.g., as separate threads). Whilesome of the systems and methods described herein are generally describedas being implemented in software (stored on and/or executed by generalpurpose hardware), specific hardware implementations or a combination ofsoftware and specific hardware implementations are also possible andcontemplated.

In accordance with common practice, the various features illustrated inthe drawings may not be drawn to scale. The illustrations presented inthe present disclosure are not meant to be actual views of anyparticular apparatus (e.g., device, system, etc.) or method, but aremerely idealized representations that are employed to describe variousembodiments of the disclosure. Accordingly, the dimensions of thevarious features may be arbitrarily expanded or reduced for clarity. Inaddition, some of the drawings may be simplified for clarity. Thus, thedrawings may not depict all of the components of a given apparatus(e.g., device) or all operations of a particular method.

Terms used herein and especially in the appended claims (e.g., bodies ofthe appended claims) are generally intended as “open” terms (e.g., theterm “including” should be interpreted as “including, but not limitedto,” the term “having” should be interpreted as “having at least,” theterm “includes” should be interpreted as “includes, but is not limitedto,” etc.).

Additionally, if a specific number of an introduced claim recitation isintended, such an intent will be explicitly recited in the claim, and inthe absence of such recitation no such intent is present. For example,as an aid to understanding, the following appended claims may containusage of the introductory phrases “at least one” and “one or more” tointroduce claim recitations. However, the use of such phrases should notbe construed to imply that the introduction of a claim recitation by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim recitation to embodiments containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should be interpreted to mean “at least one”or “one or more”); the same holds true for the use of definite articlesused to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, means at least two recitations, or two or more recitations).Furthermore, in those instances where a convention analogous to “atleast one of A, B, and C, etc.” or “one or more of A, B, and C, etc.” isused, in general such a construction is intended to include A alone, Balone, C alone, A and B together, A and C together, B and C together, orA, B, and C together, etc. For example, the use of the term “and/or” isintended to be construed in this manner.

Further, any disjunctive word or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” should be understood to include the possibilities of “A”or “B” or “A and B.”

However, the use of such phrases should not be construed to imply thatthe introduction of a claim recitation by the indefinite articles “a” or“an” limits any particular claim containing such introduced claimrecitation to embodiments containing only one such recitation, even whenthe same claim includes the introductory phrases “one or more” or “atleast one” and indefinite articles such as “a” or “an” (e.g., “a” and/or“an” should be interpreted to mean “at least one” or “one or more”); thesame holds true for the use of definite articles used to introduce claimrecitations.

Additionally, the use of the terms “first,” “second,” “third,” etc., arenot necessarily used herein to connote a specific order or number ofelements. Generally, the terms “first,” “second,” “third,” etc., areused to distinguish between different elements as generic identifiers.Absence a showing that the terms “first,” “second,” “third,” etc.,connote a specific order, these terms should not be understood toconnote a specific order. Furthermore, absence a showing that the terms“first,” “second,” “third,” etc., connote a specific number of elements,these terms should not be understood to connote a specific number ofelements. For example, a first widget may be described as having a firstside and a second widget may be described as having a second side. Theuse of the term “second side” with respect to the second widget may beto distinguish such side of the second widget from the “first side” ofthe first widget and not to connote that the second widget has twosides.

All examples and conditional language recited herein are intended forpedagogical objects to aid the reader in understanding the invention andthe concepts contributed by the inventor to furthering the art, and areto be construed as being without limitation to such specifically recitedexamples and conditions. Although embodiments of the present disclosurehave been described in detail, it should be understood that the variouschanges, substitutions, and alterations could be made hereto withoutdeparting from the spirit and scope of the present disclosure.

What is claimed is:
 1. A method comprising: obtaining audio of acommunication session between a first device of a first user and asecond device of a second user, the communication session configured forverbal communication such that the audio includes first audio capturedby the first device and second audio captured by the second device;identifying one or more first characteristics of the first audiocaptured by the first device; obtaining a transcription of the secondaudio captured by the second device; identifying one or more first wordsin the transcription indicating the first user lacks understanding ofthe second audio; and based on the one or more first characteristics ofthe first audio and the one or more first words, determining an accuracyof the transcription of the second audio.
 2. The method of claim 1,wherein the first audio includes first speech of the first user and thefirst characteristics of the first audio include: a tone, a volume, apitch, an inflection, a timbre, or a speed of the first speech.
 3. Themethod of claim 1, further comprising: obtaining a second transcriptionof the first audio; and identifying one or more second words in thesecond transcription indicating the first user lacks understanding ofthe second audio, wherein the accuracy of the transcription of thesecond audio is determined further based on the second words.
 4. Themethod of claim 1, further comprising identifying one or more secondcharacteristics of the second audio, wherein determining the accuracy ofthe transcription of the second audio is further based on the one ormore second characteristics of the second audio.
 5. The method of claim1, wherein the one or more first characteristics of the first audio areidentified at a first time, the method further comprising: identifyingone or more second characteristics of the first audio at a second timedifferent than the first time; and determining a difference between theone or more first characteristics and the one or more secondcharacteristics, wherein the accuracy of the transcription of the secondaudio is determined based on the determined difference.
 6. The method ofclaim 1, further comprising obtaining one or more characteristics ofsound in an environment of the first user, wherein the determining theaccuracy of the transcription of the second audio is further based onthe one or more characteristics of the sound in the environment.
 7. Themethod of claim 1, further comprising determining a topic of thecommunication session, wherein the determining the accuracy of thetranscription of the second audio is further based on the topic.
 8. Amethod comprising: obtaining audio of a communication session between afirst device of a first user and a second device of a second user, thecommunication session configured for verbal communication such that theaudio includes first audio captured by the first device and second audiocaptured by the second device; identifying one or more firstcharacteristics of the first audio captured by the first device;obtaining a transcription of the second audio captured by the seconddevice; and based on the one or more first characteristics of the firstaudio, determining an accuracy of the transcription of the second audio.9. The method of claim 8, wherein the first audio includes first speechof the first user and the first characteristics of the first audioinclude: a tone, a volume, a pitch, an inflection, a timbre, or a speedof the first speech.
 10. The method of claim 8, further comprising:obtaining a second transcription of the first audio; and identifying oneor more words in the second transcription indicating the first userlacks understanding of the second audio, wherein the accuracy of thetranscription of the second audio is determined further based on the oneor more words.
 11. The method of claim 8, wherein the one or more firstcharacteristics of the first audio are identified at a first time, themethod further comprising: identifying one or more secondcharacteristics of the first audio at a second time different than thefirst time; and determining a difference between the one or more firstcharacteristics and the one or more second characteristics, wherein theaccuracy of the transcription of the second audio is determined based onthe determined difference.
 12. The method of claim 8, further comprisingobtaining one or more characteristics of sound in an environment of thefirst user, wherein the determining the accuracy of the transcription ofthe second audio is further based on the one or more characteristics ofthe sound in the environment.
 13. The method of claim 8, furthercomprising determining a topic of the communication session, wherein thedetermining the accuracy of the transcription of the second audio isfurther based on the topic.
 14. A system comprising: one or moreprocessors; and one or more non-transitory computer-readable mediaconfigured to store instructions that in response to being executed bythe one or more processors cause the system to perform operations, theoperations comprising: obtaining audio of a communication sessionbetween a first device of a first user and a second device of a seconduser, the communication session configured for verbal communication suchthat the audio includes first audio captured by the first device andsecond audio captured by the second device; identifying one or morefirst characteristics of the first audio captured by the first device;obtaining a transcription of the second audio captured by the seconddevice; identifying one or more first words in the transcriptionindicating the first user lacks understanding of the second audio; andbased on the one or more first characteristics of the first audio andthe one or more first words, determining an accuracy of thetranscription of the second audio.
 15. The system of claim 14, whereinthe first audio includes first speech of the first user and the firstcharacteristics of the first audio include: a tone, a volume, a pitch,an inflection, a timbre, or a speed of the first speech.
 16. The systemof claim 14, wherein the operations further comprise: obtaining a secondtranscription of the first audio; and identifying one or more secondwords in the second transcription indicating the first user lacksunderstanding of the second audio, wherein the accuracy of thetranscription of the second audio is determined further based on thesecond words.
 17. The system of claim 14, wherein the operations furthercomprise identifying one or more second characteristics of the secondaudio, wherein determining the accuracy of the transcription of thesecond audio is further based on the one or more second characteristicsof the second audio.
 18. The system of claim 14, wherein the one or morefirst characteristics of the first audio are identified at a first time,the operations further comprising: identifying one or more secondcharacteristics of the first audio at a second time different than thefirst time; and determining a difference between the one or more firstcharacteristics and the one or more second characteristics, wherein theaccuracy of the transcription of the second audio is determined based onthe determined difference.
 19. The system of claim 14, wherein theoperations further comprise obtaining one or more characteristics ofsound in an environment of the first user, wherein the determining theaccuracy of the transcription of the second audio is further based onthe one or more characteristics of the sound in the environment.
 20. Thesystem of claim 14, wherein the operations further comprise determininga topic of the communication session, wherein the determining theaccuracy of the transcription of the second audio is further based onthe topic.